This is the second part of the lesson in which we review different chart types in Data Studio,
we learn how and when to use them, plus some data visualization best practices.
Hi, this is Ahmad from Siavak with another lesson from
Google Data Studio essentials. In the previous part, we covered the scorecard, table, time
series, area chart and bar chart. If you haven't watched that, it's completely fine. You can
watch it later. In this part, we're going to cover the map, the pie charts, the scatter
chart and bubble charts, the tree map, the pivot table, and the bullet chart. Let's go
ahead and get started.
And that's a map. So with a map chart, we can visualize the difference of a metric across
different geographical locations. Here I have users by country and then the map the more
the number of users, darker would be the shade of the color. So without looking at any other
data within this dataset, we know that this is a website that gets the most of its audience
from United States. And now that we understand this, we can zoom into the United States and
see the number of users per state, like this. And here we can see California is the outlier,
with the largest number of users. Now this is the old legacy map chart in Google Data
Studio. Recently, a new map chart has been introduced. And that's the Google Map. Here
we can see users per city. The first difference that we can notice on this Google map is that
instead of shaded regions to numbers are plotted as dots and bubbles on the map. Here, each
bubble represents a city. And the size of the bubble represents the number of users
that we've had from that city. Another cool feature of a Google map is that we can pan
around and zoom in to see more details about areas that you're interested in. Here's another
example of a Google Map. Here we are seeing the users and revenue intensity worldwide.
In this chart, this size of the bubble, again represents the number of users. But the color
of the bubble represents the amount of revenue. If it's white, it means that we had basically
no revenue for that city, Florence and Italy. And if it's orange, it means that we had a
large amount of revenue from that city. So for example, this bubble is both larger in
size, meaning more users, and more orange in color, which means more revenue. Now, what
I can do is that I can click on this table to filter to Google Map, and it automatically
zooms on the United States to show me the data only from the cities within the US. This
is another example the same map, and the only thing that have changed is the styling. So
it looks much more like a Google Map. Now again, if I click the table to filter the
map only for New York City, it automatically zooms on the selected city. There was cooler
than a bar chart wasn't it. But that's all about the maps for today. And let's go see
another exciting example. a pie chart.
Pie charts are interesting, almost every data visualization expert that I know of hates
pie charts, and they have good reasons for that. Not only it takes so much unnecessary
space under reports, but also the human brain isn't really good at comparing the lens of
curved lines, or comparing the angles with different angles. Okay. That's why that the
viewers of this report will have a hard time to extract some insight and meanings from
this chart just by taking a look at the visuals. And if they want to see and compare the actual
values, they have to move the mouse around all of these sections of the pie charts, one
by one. If the report is printed, they just see this. But just like any other types of
data visualization, pie charts can be used correctly or incorrectly. My rule of thumb
is that if I can visualize my data using any other chart type, I usually don't use pie
charts. But if you have to use them if you want to use them. There are some guidelines
that you can follow to have a better pie chart that the viewers of your report can actually
understand.
First of all, the fewer categories, the better. Here's an example of 10 categories. This one
has six categories, age range, a bit more clear. This one starts to become a good example,
just three categories, we can easily see the desktop has the largest number, followed by
mobile and tablet. And then probably the best usage of a pie chart is when our dimension
only has two categories, new visitor versus returning visitor, male versus female. Now
some people they didn't like pies, so they created a blank circle within a pie to make
a donut chart, but that's basically the same the same rules apply. But what if you have
more than two or three categories? What are your alternatives? Here I have two alternatives
for you. So, instead of a pie chart, consider using a bar chart. The human brain can see
the difference of the lens of straight lines much more easily, especially if they're laid
out side by sight. And if you want to save space, we can also use 100% stacked bar chart
with just one bar. Enough about pies and donuts. Let's move on to the next chart type.
Scatter and bubble charts are usually a bit more complicated and harder to understand
for the viewer of the report. It usually takes more time to process the data presented in
this way. Try to keep your audience in mind and stay away from using more complex charts
like this like a scatter and bubble chart. Unless you know that the viewers of your report
are really familiar with this type of data, or they're ready to spend some more time to
understand the data that is visualized this way. But I'm going to just show you a few
examples so you know what's possible to do with this chart type. They're good to show
relationship between two metrics with a dimension as they break down. So for example, here I
have transactions and average order value. And we're using the US state as the breakdown
dimension. so here we can see the outlier, California, which has the largest number of
transactions and a fairly high average order value. There are four other states that have
higher average order value, but they have fewer transactions. Now we can make it even
more complicated by introducing an additional set of dimension. So for example here, now
I have the same metrics transactions, average order value, but this time I'm breaking them
down based on the browser. And also I'm using bubble size to show the ecommerce conversion
rate. So again, we can immediately see the outliers. Chrome with the largest number of
transactions, a fairly high average order value and the largest e commerce conversion
rate. Over here, Firefox with the largest average order value with just a few transactions,
and with few I mean compared to the more than 16,000 transactions on Chrome. And then the
rest of the browsers with a low number of transactions and a lower average order value.
So if the owner of this business wants to optimize their website for different browsers,
they can use this chart to quickly see where they should spend their resources. Another
example again transactions average order value, and ecommerce conversion rate as the bubble
size. But this time broken down by device category, desktop, mobile and tablet again,
we can see the desktop is responsible for the largest number of transactions and the
highest average order value. Plus it has the largest ecommerce conversion rate as well.
A different example we want to see which channel is bringing more traffic and more revenue.
And because the amount of revenue for this data set had a really wide range have somewhere
close to $1,000 to over a million dollars, I had to use the logarithmic scale for the
x axis with another scale for the horizontal axis. Each step here is 10 times more than
the previous step. So 100k 10k, a thousand, under a thousand and one. Now let's read the
data. So referral has about 100k users with more than a million in revenue. Over here,
organic search has about 350,000 users, but somewhere close to half a million in revenue.
Feel free to pause the video and try to interpret the chart. As you can see, it takes much more
brainpower and time to process a chart like this rather than a simple trendline or a bar
chart or a combo chart. Okay, that's enough time spent on the scatter and bubble chart.
Let's go and see the tree map.
Tree map lets us compare the value of one metric across the categories of one dimension
like this. It shows the high values with a darker shade of color. And also by allocating
a large area to that category. Here we can see the nest Hello doorbell was responsible
for the largest amount of revenue across all products. But something else that we notice
is that many of these are in the nest category, they are nest products. So let's take a look
at a different way of using the tree map. Now I applied two dimensions to that product
and product category. Now instead of all nest products be scattered around different places
on the chart. They're all inside this rectangle that I'm showing with my mouse pointer. Okay,
so this is all nasty. This is our apparel. This is all office product during create product
etc. So we can see the size of revenue for each category. And within our category, we
can see which one of the products had the largest amount of revenue. But as you can
see, this is too complicated. There are so many categories. A good practice for using
tree maps is to use a dimension that has fewer categories in it. Like here that if just applied
product category, so we can immediately see that nest as a category has the largest amount
of revenue across all different categories for this shop. And before we say goodbye to
tree map, here's another example of using a dimension with few categories, age range,
and see which category has the largest amount of revenue. We only have two more charts left
pivot table and bullet chart. Let's take a look at pivot tables.
Pivot Tables are good for summarizing large tables with several dimensions and metrics
into a cleaner, simpler way. Here I'm showing product revenue and quantity per product category.
And I'm using bar so we can quickly compare the numbers. Now the first difference of a
pivot table and the table is that it highlights the rows as we move our cursor across different
categories. And you might have noticed this point sign here and that in the title, I said
product revenue and quantity, which we can see per product and product category. But
the only thing that we can see here is the product category. Where is the product? If
we click on this plus sign, we can expand the pivot table. So for each category, it
can list all the products within that category. And we can see line items, the revenue for
that product only. Now if we scroll down slowly to reach the end of the next category, we
can see the subtotal for the next category for all product was close to $2 million dollars
in revenue, and close to 15 tabs in quantity sold, which is the same amount that we can
see for the whole category. If you collapse the columns, this and this. At the bottom,
we can see the grand total for all categories.
Let's take a look at another example of a pivot table. The previous one was kinda like
a normal table, but with the expandable column of product but this one is completely different.
Instead of having dimensions represented as rows and metrics represented in columns, we
have a dimension product category in rows. And we also have another dimension, the channel,
the default channel grouping, represented in columns. So for each product category,
we can see how much revenue did we make from referral? How much from direct how much from
organic search? And then for each of these categories, we can see how much did we make
in total? And over the right, we can see how much did we make from each product category,
regardless of channel again, in total, and you already know about the plus sign. If I
click on this, I can expand product category to see the product within each category. So
for each product, how much did we make from referral, how much from direct, how much from
organic search, and then how much in total. Just like the previous example, if we scroll
down to the end the next category, we can see this subtotal for that category. That's
it all about the pivot table. Let's move on to the final chart, Bullet chart.
Bullet charts are simple. They show the performance of one metric compared to the target. Plus,
we can set three different ranges, the low range, the mid range and high range using
shades of a color. Let's take a look at this one. Ecommerce conversion rate. It shows 2%
but it's because it is a compact number with no decimals, we can see that it is actually
somewhere between 1.5 and 2%. Now the target and this is set to 2% we can set the target
on whichever number that we want. It is part of the chart settings. And this is usually
something that the business owner should decide, okay, do they want 2% ecommerce conversion
rate? Is this something that they're happy with? Is this something that they are trying
to achieve? Okay. And also we can set three ranges, the low range which is usually considered
bad. So if he comes conversion rate is less than 1%. It's really bad performance. If it's
somewhere between one and 2% mid range is quite acceptable, and then 2%, and anything
above that is considered good. Now we can choose to show just one bar for this time
period, or we can choose to compare it with the previous time period. So for example,
in this case, we can see that in the previous period, the commerce conversion rate was in
the good range 2.5%. But in this time period is less than the target, but it's still in
the acceptable range. As another example, we can have the same for revenue. So they
have a target of 2 million, anything less than one and a half million is considered
bad. Anything between one and a half million and two and a half million is considered acceptable.
And then anything above that is good. In this period, they hit the target and they actually
overperformed they made close to 3 million in revenue, which is good right $3 million.
But then we see the comparison. And we noticed that in the previous period, they had made
close to $5 million in revenue. So despite hitting the target, despite being in a good
range, they're not making as much money that they used to make in the previous time period.
Now, based on this data, the owners of this business might want to actually move the target
for the next period, from 2 million to somewhere closer to three, or $4 million.
Okay, that's it. Congratulations. Thank you for staying with me. You notice that I couldn't
actually show you how to set up and configure all of these charts because that would have
taken so much time, but at least you know what's possible? And what kind of options
do you have when you want to visualize data and communicate insights to the viewers of
your reports. These are your tools for when you want to tell a story with your data set.
We've just reviewed all different chart types in Data Studio as of June 2020. As you noticed,
I couldn't actually show you how to set up and configure all of those chart types, it
would have taken too long. But at least right now we know what is possible in Data Studio
in terms of data visualization, and what kind of tools we have to communicate different
kind of insight to the end user profile report. It's good to know that it's possible for anyone
to create a custom chart type for Data Studio. They're called Community visualizations. And
that's the subject of our next lesson. Did you learn anything new today? Feel free to
test your knowledge by taking the quiz or continue to the next lesson. As always, you
can find those links in the description below. If you're new to measureschool, make sure
to subscribe so you can get notified of future videos that we publish. And if you have any
questions, please feel free to leave a comment. I look forward to seeing you next time.